Optoelectronic neuromorphic thin-film transistors capable of selective attention and with ultra-low power dissipation

Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

/pdf/memristive-artificial-synapses-for-neuromorphic-computing-1w45fvlu4o.pdf

Memristive Artificial Synapses for Neuromorphic Computing

Organic photodetectors with UV-sensitivity are of great potential for various optoelectronic applications. Integration of high charge carrier mobility, long exciton diffusion length as well as unique UV-sensitivity for active materials is crucial for construction of UV-sensitive devices with high performance, however, very few organic semiconductors can integrate these properties simultaneously. Herein, two novel organic semiconductors containing large steric hindrance triphenylamine groups, 1,6-distriphenylamineethynylpyrene (1,6-DTEP) and 2,7-distriphenylamineethynylpyrene (2,7-DTEP) are designed and synthesized. It demonstrates that the single crystals of both 1,6-DTEP and 2,7-DTEP exhibit superior integrated optoelectronic properties of high charge carrier mobility, unique UV absorption, high photoluminescence quantum yields as well as small exciton binding energies. Organic phototransistors constructed using 1,6-DTEP and 2,7-DTEP single crystals show ultrasensitive performance with ultra-high photoresponsivity of 2.86 × 106 and 1.04 × 105 A W-1 , detectivity (D*) of above 1.49 × 1018 and 5.28 × 1016 Jones under 370 nm light illumination, respectively. It indicates the great potential of 1,6-DTEP and 2,7-DTEP-based phototransistors for organic UV-photodetector applications and also provides a new design strategy to develop series of better performance UV photoelectric organic materials for related research in organic optoelectronics.

Organic UV-Sensitive Phototransistors Based on Distriphenylamineethynylpyrene Derivatives with Ultra-High Detectivity Approaching 1018.

Multi-spectral gate-triggered heterogeneous photonic neuro-transistors for power-efficient brain-inspired neuromorphic computing

Drawing inspiration from biology, neuromorphic systems are of great interest in direct interaction and efficient processing of analogue signals in the real world and could be promising for the development of smart sensors. Here, we demonstrate an artificial sensory neuron consisting of an InGaZnO4 (IGZO4)-based optical sensor and NbOx-based oscillation neuron in series, which can simultaneously sense the optical information even beyond the visible light region and encode them into electrical impulses. Such artificial vision sensory neurons can convey visual information in a parallel manner analogous to biological vision systems, and the output spikes can be effectively processed by a pulse coupled neural network, demonstrating the capability of image segmentation out of a complex background. This study could facilitate the construction of artificial visual systems and pave the way for the development of light-driven neurorobotics, bioinspired optoelectronics, and neuromorphic computing.

Spike Encoding with Optic Sensory Neurons Enable a Pulse Coupled Neural Network for Ultraviolet Image Segmentation.

A visible-blind ultraviolet (UV) photodetector was designed based on a three-terminal electronic device of thin-film transistor (TFT) coupled with two-terminal p–n junction optoelectronic device, in hope of combining the beauties of both of the devices together. Upon the uncovered back-channel surface of amorphous indium–gallium–zinc-oxide (IGZO) TFT, we fabricated PEDOT:PSS/SnOx/IGZO heterojunction structure, through which the formation of a p–n junction and directional carrier transfer of photogenerated carriers were experimentally validated. As expected, the photoresponse characteristics of the newly designed photodetector, with a photoresponsivity of 984 A/W at a wavelength of 320 nm, a UV–visible rejection ratio up to 3.5 × 107, and a specific detectivity up to 3.3 × 1014 Jones, are not only competitive compared to the previous reports but also better than those of the pristine IGZO phototransistor. The hybrid photodetector could be operated in the off-current region with low supply voltages (<0.1 V)...

Wen Shi

Papers

High-Performance Visible-Blind Ultraviolet Photodetector Based on IGZO TFT Coupled with p–n Heterojunction